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Abstract Background and aims Stroke is the leading cause of adult-onset disability. Although clinical factors influence stroke outcome, there is a significant variability among individuals that may be attributed to genetics and epigenetics, including DNA methylation (DNAm). We aimed to study the association between DNAm and stroke prognosis. Methods and results To that aim, we conducted a two-phase study (discovery-replication and meta-analysis) in Caucasian patients with ischemic stroke from two independent centers (BasicMar [discovery, N = 316] and St. Pau [replication, N = 92]). Functional outcome was assessed using the modified Rankin Scale (mRS) at three months after stroke, being poor outcome defined as mRS > 2. DNAm was determined using the 450K and EPIC BeadChips in whole-blood samples collected within the first 24 h. We searched for differentially methylated positions (DMPs) in 370,344 CpGs, and candidates below p-value

This comprehensive review explores the emerging field of epigenetics in intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (aSAH). Despite recent advancements, the high mortality of aSAH needs an understanding of its underlying pathophysiology, where epigenetics plays a crucial role. This review synthesizes the current knowledge, focusing on three primary epigenetic mechanisms: DNA methylation, non-coding RNA (ncRNA), and histone modification in IA and aSAH. While DNA methylation studies are relatively limited, they suggest a significant role in the pathogenesis and prognosis of IA and aSAH, highlighting differentially methylated positions in genes presumably involved in these pathologies. However, methodological limitations, including small sample sizes and a lack of diverse population studies, temper these results. The role of ncRNAs, particularly miRNAs, has been more extensively studied, but there are still few studies focused on histone modifications. Despite methodological challenges and inconsistent findings, these studies underscore the involvement of miRNAs in key pathophysiological processes, including vascular smooth muscle regulation and the inflammatory response. This review emphasizes methodological challenges in epigenetic research, advocating for large-scale epigenome-wide association studies integrating genetic and environmental factors, along with longitudinal studies. Such research could unravel the complex mechanisms behind IA and aSAH, guiding the development of targeted therapeutic approaches.

Age acceleration (Age-A) is a useful tool that is able to predict a broad range of health outcomes. It is necessary to determine DNA methylation levels to estimate it, and it is known that Age-A is influenced by environmental, lifestyle, and vascular risk factors (VRF). The aim of this study is to estimate the contribution of these easily measurable factors to Age-A in patients with cerebrovascular disease (CVD), using different machine learning (ML) approximations, and try to find a more accessible model able to predict Age-A. We studied a CVD cohort of 952 patients with information about VRF, lifestyle habits, and target organ damage. We estimated Age-A using Hannum’s epigenetic clock, and trained six different models to predict Age-A: a conventional linear regression model, four ML models (elastic net regression (EN), K-Nearest neighbors, random forest, and support vector machine models), and one deep learning approximation (multilayer perceptron (MLP) model). The best-performing models were EN and MLP; although, the predictive capability was modest (R2 0.358 and 0.378, respectively). In conclusion, our results support the influence of these factors on Age-A; although, they were not enough to explain most of its variability.

In this manuscript we studied the relationship between WMH and biological age (B-age) in patients with acute stroke. We included in this study 247 patients with acute stroke recruited at Hospital del Mar having both epigenetic (DNA methylation) and magnetic resonance imaging data. WMH were measured using a semi-automated method. B-age was calculated using two widely used methods: the Hannum and Horvath formulas. We used multiple linear regression models to interrogate the role of B-age on WMH volume after adjusting for chronological age (C-age) and other covariables. Average C-age of the sample was 68.4 (±11.8) and we observed a relatively high median WMH volume (median = 8.8 cm3, Q1–Q3 = 4.05–18.8). After adjusting for potential confounders, we observed a significant effect of B-ageHannum on WMH volume (βHannum = 0.023, p-value = 0.029) independently of C-age, which remained significant (βC-age = 0.021, p-value = 0.036). Finally, we performed a mediation analysis, which allowed us to discover that 42.7% of the effect of C-age on WMH is mediated by B-ageHannum. On the other hand, B-ageHoarvath showed no significant associations with WMH after being adjusted for C-age. In conclusion, we show for the first time that biological age, measured through DNA methylation, contributes substantially to explain WMH volumetric burden irrespective of chronological age.